Detect Crypto Chargeback Fraud
“Which accounts that recently purchased crypto will file a fiat chargeback in the next 14 days?”
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A real-world example
Which accounts that recently purchased crypto will file a fiat chargeback in the next 14 days?
Scammers buy crypto with bank accounts, transfer crypto to self-custody wallets, then dispute the fiat charge. The bank reverses fiat while crypto is gone forever. This exploits the fundamental asymmetry between reversible fiat and irreversible crypto. Losses exceed $2M+ per quarter at mid-size neobanks.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo’s backward time window filters to accounts with recent crypto purchases, then predicts fiat disputes. It sees the cross-domain pattern: Account A001 bought $5,200 on Coinbase, transferred to self-custody within hours, and is now likely to dispute the fiat charge. Rules that look at fiat-only or crypto-only data miss this cross-rail pattern.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
Accounts
| account_id | account_holder | account_type | kyc_date |
|---|---|---|---|
| A001 | Alice M. | Retail | 2023-01-15 |
| A002 | Bob C. | Retail | 2024-06-10 |
Crypto Purchases
| purchase_id | account_id | exchange | fiat_amount | timestamp |
|---|---|---|---|---|
| CP01 | A001 | Coinbase | 5,200 | 2025-01-10 |
| CP02 | A002 | Binance | 12,000 | 2025-01-12 |
Disputes
| dispute_id | account_id | purchase_id | reason | timestamp |
|---|---|---|---|---|
| D01 | A001 | CP01 | unauthorized | 2025-01-18 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT COUNT(DISPUTES.*, 0, 14, days) > 0 FOR EACH ACCOUNTS.ACCOUNT_ID WHERE COUNT(CRYPTO_PURCHASES.*, -7, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| A001 | 2025-02-01 | True | 0.82 |
| A002 | 2025-02-01 | True | 0.71 |
| A003 | 2025-02-01 | False | 0.04 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001 (Alice M.)
Predicted: 82% crypto chargeback probability
Top contributing features
Crypto purchase fiat_amount (7d)
$5,200
40% attribution
Dispute reason
unauthorized
24% attribution
Exchange
Coinbase
16% attribution
KYC date recency
2+ years ago
12% attribution
Account type
Retail
8% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2-3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Delay fiat settlement on high-risk crypto purchases. Hold funds 72 hours on accounts with >70% dispute probability. Prevent $2M+ in irreversible losses per quarter.
Related scenarios
Explore more fraud predictions
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
